OPBench：應對鴉片類藥物危機的圖數據基準

OPBench: A Graph Benchmark to Combat the Opioid Crisis

February 16, 2026

作者: Tianyi Ma, Yiyang Li, Yiyue Qian, Zheyuan Zhang, Zehong Wang, Chuxu Zhang, Yanfang Ye

cs.AI

摘要

鴉片類藥物濫用危機持續肆虐全球社區，不僅使醫療體系不堪重負、造成家庭破碎，更亟需運算技術的緊急應對方案。為對抗這場致命的鴉片危機，圖學習方法已成為建模複雜藥物相關現象的嶄新範式。然而當前存在關鍵缺口：尚無系統性評估這些方法在真實鴉片危機場景中表現的綜合基準。為此，我們推出首個綜合性鴉片危機基準平台OPBench，涵蓋三大關鍵應用領域的五套數據集：基於醫療理賠數據的用藥過量檢測、源自數位平台的非法藥物交易識別，以及透過飲食模式預測藥物濫用行為。具體而言，OPBench整合了異質圖與超圖等多元圖結構，以完整保留藥物數據間豐富而複雜的關聯資訊。為解決數據稀缺問題，我們攜手領域專家與權威機構，在遵循隱私與倫理規範下進行數據策展與標註。此外，我們建立包含標準化流程、預定義數據分割與可復現基線的統一評估框架，以促進圖學習方法間的公平系統性比較。透過大規模實驗，我們深入剖析現有圖學習方法的優勢與局限，為未來抗擊鴉片危機的研究提供可行見解。相關源代碼與數據集已開源於：https://github.com/Tianyi-Billy-Ma/OPBench。

English

The opioid epidemic continues to ravage communities worldwide, straining healthcare systems, disrupting families, and demanding urgent computational solutions. To combat this lethal opioid crisis, graph learning methods have emerged as a promising paradigm for modeling complex drug-related phenomena. However, a significant gap remains: there is no comprehensive benchmark for systematically evaluating these methods across real-world opioid crisis scenarios. To bridge this gap, we introduce OPBench, the first comprehensive opioid benchmark comprising five datasets across three critical application domains: opioid overdose detection from healthcare claims, illicit drug trafficking detection from digital platforms, and drug misuse prediction from dietary patterns. Specifically, OPBench incorporates diverse graph structures, including heterogeneous graphs and hypergraphs, to preserve the rich and complex relational information among drug-related data. To address data scarcity, we collaborate with domain experts and authoritative institutions to curate and annotate datasets while adhering to privacy and ethical guidelines. Furthermore, we establish a unified evaluation framework with standardized protocols, predefined data splits, and reproducible baselines to facilitate fair and systematic comparison among graph learning methods. Through extensive experiments, we analyze the strengths and limitations of existing graph learning methods, thereby providing actionable insights for future research in combating the opioid crisis. Our source code and datasets are available at https://github.com/Tianyi-Billy-Ma/OPBench.